19,736 research outputs found
CSGNet: Neural Shape Parser for Constructive Solid Geometry
We present a neural architecture that takes as input a 2D or 3D shape and
outputs a program that generates the shape. The instructions in our program are
based on constructive solid geometry principles, i.e., a set of boolean
operations on shape primitives defined recursively. Bottom-up techniques for
this shape parsing task rely on primitive detection and are inherently slow
since the search space over possible primitive combinations is large. In
contrast, our model uses a recurrent neural network that parses the input shape
in a top-down manner, which is significantly faster and yields a compact and
easy-to-interpret sequence of modeling instructions. Our model is also more
effective as a shape detector compared to existing state-of-the-art detection
techniques. We finally demonstrate that our network can be trained on novel
datasets without ground-truth program annotations through policy gradient
techniques.Comment: Accepted at CVPR-201
Play and Learn: Using Video Games to Train Computer Vision Models
Video games are a compelling source of annotated data as they can readily
provide fine-grained groundtruth for diverse tasks. However, it is not clear
whether the synthetically generated data has enough resemblance to the
real-world images to improve the performance of computer vision models in
practice. We present experiments assessing the effectiveness on real-world data
of systems trained on synthetic RGB images that are extracted from a video
game. We collected over 60000 synthetic samples from a modern video game with
similar conditions to the real-world CamVid and Cityscapes datasets. We provide
several experiments to demonstrate that the synthetically generated RGB images
can be used to improve the performance of deep neural networks on both image
segmentation and depth estimation. These results show that a convolutional
network trained on synthetic data achieves a similar test error to a network
that is trained on real-world data for dense image classification. Furthermore,
the synthetically generated RGB images can provide similar or better results
compared to the real-world datasets if a simple domain adaptation technique is
applied. Our results suggest that collaboration with game developers for an
accessible interface to gather data is potentially a fruitful direction for
future work in computer vision.Comment: To appear in the British Machine Vision Conference (BMVC), September
2016. -v2: fixed a typo in the reference
Adversarial nets with perceptual losses for text-to-image synthesis
Recent approaches in generative adversarial networks (GANs) can automatically
synthesize realistic images from descriptive text. Despite the overall fair
quality, the generated images often expose visible flaws that lack structural
definition for an object of interest. In this paper, we aim to extend state of
the art for GAN-based text-to-image synthesis by improving perceptual quality
of generated images. Differentiated from previous work, our synthetic image
generator optimizes on perceptual loss functions that measure pixel, feature
activation, and texture differences against a natural image. We present
visually more compelling synthetic images of birds and flowers generated from
text descriptions in comparison to some of the most prominent existing work
Retrosynthetic reaction prediction using neural sequence-to-sequence models
We describe a fully data driven model that learns to perform a retrosynthetic
reaction prediction task, which is treated as a sequence-to-sequence mapping
problem. The end-to-end trained model has an encoder-decoder architecture that
consists of two recurrent neural networks, which has previously shown great
success in solving other sequence-to-sequence prediction tasks such as machine
translation. The model is trained on 50,000 experimental reaction examples from
the United States patent literature, which span 10 broad reaction types that
are commonly used by medicinal chemists. We find that our model performs
comparably with a rule-based expert system baseline model, and also overcomes
certain limitations associated with rule-based expert systems and with any
machine learning approach that contains a rule-based expert system component.
Our model provides an important first step towards solving the challenging
problem of computational retrosynthetic analysis
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